A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data

Gene microarray technology provides an efficient way to diagnose cancer. However, microarray gene expression data face the challenges of high-dimension, small-sample, and multi-class imbalance. The coupling of these challenges leads to inaccurate results when using traditional feature selection and classification algorithms. Due to fast learning speed and good classification performance, deep neural network such as generative adversarial network has been proven one of the best classification algorithms, especially in bioinformatics domain. However, it is limited to binary application and inefficient in processing high-dimensional sparse features. This paper proposes a multi-classification generative adversarial network model combined with features bundling (MGAN-FB) to handle the coupling of high-dimension, small-sample, and multi-class imbalance for gene microarray data classification at both feature and algorithmic levels. At feature level, a deep encoder structure combining feature bundling (FB) mechanism and squeeze and excite (SE) mechanism, is designed for the generator. So, the sparsity, correlation and consequence of high-dimension features are all taken into consideration for adaptive features extraction. It achieves effective dimensionality reduction without transitional information loss. At algorithmic level, a softmax module coupled with multi-classifier are introduced into the discriminator, with a new objective function is distinctively designed for the proposed MGAN-FB model, considering encode loss, reconstruction loss, discrimination loss and multi-classification loss. We extend generative adversaria framework from the binary classification to the multi-classification field. Experiments are performed on eight open-source gene microarray datasets from classification performance, running time and non-parametric tests, which demonstrate that the proposed method has obvious advantages over other 7 compared methods.